Music Genre Classification Using Feature Subset Search

Jihae Yoon, Hyunki Lim, and Dae-Won Kim

Abstract—With the growing number of digital music, the automatic genre recognition problem has been receiving the spotlight in music retrieval information field. A large number of musical acoustic features are reported to degrade the genre classification performance and lead to heavy computational cost. In this paper, we propose a new method for selecting genre-discriminative feature subset from a large number of musical features. We show that the proposed method is able to improve the genre recognition accuracy compared to the traditional selection method.